Research on Transfer Learning and Algorithm Fairness Calibration in Cross-Market Credit Scoring

When credit scoring models are applied across markets, their transfer performance often deteriorates due to borrower feature distribution and sample heterogeneity. This paper systematically evaluates the transfer effectiveness of cross-market credit scoring models using 2 million credit card account records from China and the United States. It proposes a fairness calibration method combining domain-adaptive weighting with monotonic constraints. Experimental results show that directly transferred models experience a 7.2 percentage point decline in AUC in the target market. After applying the proposed method, performance loss is reduced to 1.3 percentage points while group fairness metrics (equity gap) improve by 41%. The study demonstrates that integrating transfer learning with fairness constraints not only enhances the robustness and generalizability of cross-domain scoring models but also ensures credit fairness across different groups. This provides an effective pathway for applying data science in cross-border credit accessibility and compliance modeling.